Sunday, January 12, 2014

In a previous post titled Toward Polyglot Programming on the Java Virtual Machine (JVM), I described my preliminary exploration of the other languages and frameworks on the JVM including Groovy, Gradle, Grails, Scala, Akka, Clojure, and the Play Framework. I made the switch from Maven to Gradle, a Groovy-based build language that combines the best of Ant and Maven. I was seduced by the scaffolding capabilities of Grails, but decided to make the jump to Scala and functional programming. So I have been navigating in Scala land recently. In this post, I described my journey.

I am still learning, but I can tell you that I never had so much fun learning a new programming language. It probably has something to do with the use of pure mathematical functions in Scala. I did spend a year studying pure mathematics at the University of Abomey-Calavi after graduating from high school. My first exposure to functional programming was with XSLT and XQuery and I very much enjoyed programming without side effects when using those languages. XSLT 3.0 is a fully-fledged functional programming language with support for functions as first class values and high-order functions. XQuery 3.0 is a typed functional language for processing and querying XML data.

Scala is a complex and ambitious language as it supports both object-oriented and functional programming. When I started to learn Scala, I took the wrong directions several times and had to make several U-turns. So the following steps have been effective for me:

The Coursera class Functional Programming Principles in Scalataught by Martin Odersky (the designer of Scala) is a good place to start. It explains the motivations behind Scala and emphasizes its mathematical and functional nature without trying to map pre-existing knowledge (of Java or Python, or any other language) to Scala. This is a refreshing approach because Scala is a different language although it can interoperate with Java. A good companion to this course is the book Programming in Scala: A Comprehensive Step-by-Step Guide, 2nd Edition by Martin Odersky, Lex Spoon and Bill Venners.

If you're a Java programmer moving to Scala, then the book Scala for the Impatient by Cay S. Horstmann would be a good reference.

If you're interested in building a web application in Scala, then I would recommend the book Play for Scala by Peter Hilton, Erik Bakker and Francisco Canedo. Scala and Play come with their own ecosystem of tools. This includes a Scala-based build system called sbt (simple build tool), testing tools (like Spec2 and ScalaTest), IDEs (Eclipse-based Scala IDE and IntelliJ), database drivers (like ReactiveMongo and Slick), and authentication/authorization (SecureSocial and Deadbolt). Play has first-class support for JSON and REST, supports asynchronous responses (based on the concepts of "Future" and "Promise"), reactive programming with Akka, caching, iteratees (for processing large streams of data), and real-time push-based technologies like WebSockets and Server-Sent Events.

A this point, if you decide to dive into the deep waters of Scala, you might want to consider learning Reactive Programming and purely functional data structures.

There is a second Scala course at Coursera titled Principles of Reactive Programming taught by Martin Odersky, Erik Meijer, and Roland Kuhn. The Reactive Manifesto makes the case for a new breed of applications called "Reactive Applications". According to the manifesto, the Reactive Application architecture allows developers to build "systems that are event-driven, scalable, resilient, and responsive." In Scala land, there are currently two leading frameworks that support Reactive Programming: Akka and RxJava. The latter is a library for composing asynchronous and event-based programs using observable sequences. RxJava is a Java port (with a Scala adaptor) of the original Rx (Reactive Extensions) for .NET created by Erik Meijer. Based on the Actor Model, Akka is a framework for building highly concurrent, asynchronous, distributed, and fault tolerant event-driven applications on the JVM (it supports both Java and Scala).

For purely functional data structures, there is a Scala-based library called Scalaz. The book Functional Programming in Scala by Paul Chiusano and Rúnar Bjarnason is a good resource for exploring Scalaz.

For me, Big Data is real, not just a buzzword. I believe in analyzing humongous amounts of data to find hidden patterns and obtain insight for solving complex problems. Dean Wampler called copious data, the killer app for functional programming. Scalding by Twitter can be used for writing MapReduce jobs in Scala. Apache Spark which is written in Scala can run Machine Learning programs up to 100x faster than Hadoop MapReduce in memory. A talk titled Why Spark is the Next Top Compute Modelby Dean Wampler explains why Spark has emerged as the most likely replacement for MapReduce in Hadoop applications.

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The opinions expressed in this blog are solely my own.

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